Blended Training on Scientific Software: A Study on How Scientific Data are Generated


  • Efrosyni-Maria Skordaki Athabasca University
  • Susan Bainbridge Athabasca University



blended learning, grounded theory, scientific software, training, distance learning, snowball sampling, purposive sampling


This paper presents the results of a research study on scientific software training in blended learning environments. The investigation focused on training approaches followed by scientific software users whose goal is the reliable application of such software. A key issue in current literature is the requirement for a theory-substantiated training framework that will support knowledge sharing among scientific software users. This study followed a grounded theory research design in a qualitative methodology. Snowball sampling as well as purposive sampling methods were employed. Input from respondents with diverse education and experience was collected and analyzed with constant comparative analysis. The scientific software training cycle that results from this research encapsulates specific aptitudes and strategies that affect the users’ in-depth understanding and professional growth regarding scientific software applications. The findings of this study indicate the importance of three key themes in designing training methods for successful application of scientific software: (a) responsibility in comprehension; (b) discipline; and (c) ability to adapt.

Author Biographies

Efrosyni-Maria Skordaki, Athabasca University

Researcher, Department of Civil Engineering, Royal Military College of Canada

Susan Bainbridge, Athabasca University

Instructor, Centre for Distance Education, Athabasca University



How to Cite

Skordaki, E.-M., & Bainbridge, S. (2018). Blended Training on Scientific Software: A Study on How Scientific Data are Generated. The International Review of Research in Open and Distributed Learning, 19(2).



Research Articles